def _create_states(tracker: DialogueStateTracker, domain: Domain) -> List[State]:
"""Create states for the given tracker.
Args:
tracker: a :class:`rasa.core.trackers.DialogueStateTracker`
domain: a :class:`rasa.core.domain.Domain`
Returns:
a list of states
"""
return tracker.past_states(domain)
def _create_states(
self,
tracker: DialogueStateTracker,
domain: Domain,
is_binary_training: bool = False,
) -> List[Dict[Text, float]]:
"""Create states: a list of dictionaries.
If use_intent_probabilities is False (default behaviour),
pick the most probable intent out of all provided ones and
set its probability to 1.0, while all the others to 0.0.
"""
states = tracker.past_states(domain)
# during training we encounter only 1 or 0
if not self.use_intent_probabilities and not is_binary_training:
bin_states = []
for state in states:
# copy state dict to preserve internal order of keys
bin_state = dict(state)
best_intent = None
best_intent_prob = -1.0
for state_name, prob in state:
if state_name.startswith("intent_"):
if prob > best_intent_prob:
# finding the maximum confidence intent
if best_intent is not None:
# delete previous best intent
del bin_state[best_intent]
best_intent = state_name
best_intent_prob = prob
else:
# delete other intents
del bin_state[state_name]
if best_intent is not None:
# set the confidence of best intent to 1.0
bin_state[best_intent] = 1.0
bin_states.append(bin_state)
return bin_states
else:
return [dict(state) for state in states]
